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On channel estimation using superimposed training and first-order statistics

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2 Author(s)
Tugnait, J.K. ; Dept. of Electr. & Comput. Eng., Auburn Univ., AL, USA ; Luo, Weilin

Channel estimation for single-input multiple-output (SIMO) time-invariant channels is considered using only the first-order statistics of the data. A periodic (nonrandom) training sequence is added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. Recently superimposed training has been used for channel estimation assuming no mean-value uncertainty at the receiver and using periodically inserted pilot symbols. We propose a different method that allows more general training sequences and explicitly exploits the underlying cyclostationary nature of the periodic training sequences. We also allow mean-value uncertainty at the receiver. Illustrative computer simulation examples are presented.

Published in:

Communications Letters, IEEE  (Volume:7 ,  Issue: 9 )

Date of Publication:

Sept. 2003

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